Online Anomaly Detection under Adversarial Impact

Marius Kloft, Pavel Laskov ; JMLR W&CP 9:405-412, 2010.

Abstract

Security analysis of learning algorithms is gaining increasing importance, especially since they have become target of deliberate obstruction in certain applications. Some security-hardened algorithms have been previously proposed for supervised learning; however, very little is known about the behavior of anomaly detection methods in such scenarios. In this contribution, we analyze the performance of a particular method---online centroid anomaly detection---in the presence of adversarial noise. Our analysis addresses three key security-related issues: derivation of an optimal attack, analysis of its efficiency and constraints. Experimental evaluation carried out on real HTTP and exploit traces confirms the tightness of our theoretical bounds.